Some time ago, David Berry introduced the term ‘infrasomatization’ (Berry 2016) which he defines as the production of constitutive infrastructures; specifically the way that digital algorithms are deployed and change existing infrastructures, and how they alter rationalities by introducing computational interdependencies and structural brittleness into our systems (Berry 2018). In the process, he has just coined another new term: the Datanthropocene, the data-intensive society. This is closely linked to ‘big data’ approaches, data-intensive science, and he suggests that it “creates new economic structures but also new social realities and data-intensive subjectivities and hence new problems for society to negotiate”.
Of course, debates continue about the Anthropocene, not least whether or not it can even be defined as a specific epoch – does it start with the atomic era, for instance, or maybe even with the introduction of agriculture, or is it primarily associated with human-created climate change, pollution and extinctions?
If a visualisation is to be perceived as realistic, is it increasingly required to respond to the viewer’s actions? Is static visualisation becoming old hat? Has interactivity become a necessary part of engendering perception, action, and emotion in our response to a visualisation? And what do we mean by interactivity?
Of course, interactivity may take various forms. For instance, it may entail navigation facilities: an ability to change the viewpoint, to move through the visualisation. It may also entail manipulation facilities: the ability to modify the visualisation, to move and re-organise elements. But what are we actually interacting with?
Evidently we see a visual representation or simulation of an environment so we are interacting with that simulation. But this implies a single interface, between us as the physical embodied viewer/actor and the visualisation. Indeed, Virtual Reality is characterised as the transparent invisible interface which is all-encompassing and three-dimensional; the user is surrounded by an immersive, total simulation in which the interface both disappears and becomes the experienced simulation at one and the same time (Pold 2005). But is this true?
We sometimes underestimate the impact of digital data on archaeology because we have become so accustomed to the capture, processing, and analysis of data using our digital tools. Of course, archaeology is by no means alone in this respect. For example, Sandra Rendgren, who writes about data visualisation, infographics and interactive media, recently pointed to the creation of a new genre of journalism that has arisen from the availability of digital data and the means to analyse them (2018a). But this growth in reliance on digital data should lead to a re-consideration of what we actually mean by data. Indeed, Sandra Rendgren suggests that the term ‘data’ can be likened to a transparent fluid – “always used but never much reflected upon” – because of its ubiquity and apparent lack of ambiguity (2018b).
Is everything in archaeology computable? Does digital archaeology reach to the furthest corners of archaeology, or are there limits to what can be addressed digitally or computationally? This was a question that came up at the CAA conference in Tübingen earlier this year: Juan Barceló argued that the term ‘digital’ was fundamentally associated with numbers and formal logics, and that this constituted an unrealised ‘dark side’ to digital archaeology which required us
… to rebuild the way we think about the connection from the past to the present using a new language – mathematics, geometry – that should allow new explanations. The trouble is that most researchers do not know the language nor the tool, and they are still linked to a traditional way of doing things. (Barceló 2018).
Juan’s proposal was met with a mixed response: while some (a few) recognised this and agreed that the future success of digital archaeology lay in this language of mathematics and associated analytical methods (see, for example, Iza Romanowska’s (2018) abstract in the same conference session), others clearly felt that they did not recognise this view of digital archaeology. So why was the response so ambivalent?
Although there has been a dramatic growth in the development of autonomous vehicles and consequent competition between different companies and different methodologies, and despite the complexities of the task, the number of incidents remains remarkably small though no less tragic where the death of the occupants or other road users is involved. Of course, at present autonomous cars are not literally autonomous in the sense that a human agent is still required to be available to intervene, and accidents involving such vehicles are usually a consequence of the failure of the human component of the equation not reacting as they should. A recent fatal accident involving a Tesla Model X (e.g. Hruska 2018) has resulted in some push-back by Tesla who have sought to emphasise that the blame lies with the deceased driver rather than with the technology. One of the company’s key concerns in this instance appears to be the defence of the functionality of their Autopilot system, and in relation to this, a rather startling comment on the Tesla blog recently stood out:
No one knows about the accidents that didn’t happen, only the ones that did. The consequences of the public not using Autopilot, because of an inaccurate belief that it is less safe, would be extremely severe. (Tesla 2018).
In the face of the controversy surrounding Facebook/Cambridge Analytica, and in part as a response to the loss of trust in big tech companies (for example, Chakravorti 2018 and Yao 2018), there’s been some discussion which has sought to revisit the original ideals of the World Wide Web and hypertext. Anil Dash recently suggested:
the time is perfect to revisit a few of the overlooked gems from past eras. Perhaps modern versions of these concepts could be what helps us rebuild the web into something that has the potential, excitement, and openness that got so many of us excited about it in the first place.
That seems a rather forlorn hope, perhaps, but his revisiting of core concepts such as ‘View Source’, ‘Authoring’, and ‘Transclusion’ rang bells in my mind and led me to exhume a paper I gave back in 2004 in a session on Archaeology and the Electronic Word at the ‘Tartan TAG’ conference in Glasgow (amazingly the programme and abstracts, if not the website, are still available via https://www.antiquity.ac.uk/tag). At that time, I suggested that discussion of hypertext within archaeology had been relatively limited, especially in relation to issues such as access, power, communication and knowledge (which admittedly overlooked the contributions on digital publication in Internet Archaeology 6 (1999) for instance). This was despite the number of archaeological theorists who were enthusiastic proponents of hypertext in archaeology. For example, Ian Hodder wrote of enhanced participation and the erosion of hierarchical systems of archaeological knowledge together with the emergence of a different model of knowledge based on networks and flows – an environment in which “interactivity, multivocality and reflexivity are encouraged” (Hodder 1999a and see also Hodder 1999b, 117ff). Michael Shanks wrote of the benefits of collaborative writing in his Traumwerk wiki (no longer available) and the new insights that such activity can throw up through using an environment in which anyone could create and edit web pages on a particular topic, and add or alter the content of any contributions. Cornelius Holtorf published a number of papers discussing electronic scholarship and his experience in creating and publishing a hypermedia thesis (e.g. Holtorf 1999, 2001, 2004). In contrast, I proposed that there was a significant dislocation between the rhetoric and the reality – that what was actually being presented on our screens was masquerading as something which it was not and that consequently there might be a utopian or even a fetishisitic dialectic at work.
Jeremy Bentham’s panoptic prison design (Foucault 1975, pl.4)
I was struck by a question that Colleen Morgan asked me over lunch several months ago: “Is there a need for a digital archaeology specialism in the future?”. Of course, Colleen together with Stu Eve famously declared in 2012 that “we are all digital archaeologists” (2012, 523) given the extent to which we delegate a significant share of our work and life as archaeologists to digital devices, and the way in which the digital has penetrated to the furthest reaches of the discipline.
More recently, Andre Costopoulos picked up on this in his opening editorial for the archaeology section of the Frontiers in Digital Humanities journal, essentially arguing that digital archaeology was the not-so-new ‘normal’, and that we should stop talking about it and get on with doing it. The ‘digital turn’ has already happened in archaeology: digital technologies now regularly and habitually mediate, augment, and simulate what we do.
Is the fact that ‘we’re all digital archaeologists now’ or that archaeology has ‘gone digital’ simply a sign of our success? Should we now meekly accept the need to move on and become properly reintegrated in archaeology as our digital tools have already done? That there is no need for a digital archaeology specialism in the future?
So here’s a thing. A while ago, I asked whether there was any way to quantify the extent to which archaeologists were citing their reuse of data. I used the Thomson Reuters/Clarivate Analytics Data Citation Index (DCI) as a starting point, but it didn’t go too well … Back then, the DCI indicated that 56 of the 476 data studies derived from the UK’s Archaeology Data Service repository had apparently been cited elsewhere in the Web of Science databases (the figure is currently 58 out of 515). But I also found that the citations themselves were problematic: the citation of the published paper/volume was frequently incomplete or abbreviated, many appeared to be self-citations from within interim or final reports, in some cases the citations preceded the dates of the project being referenced, and in many instances it was possible to demonstrate that the data had been cited (in some form or other) but this had not been captured in the DCI. At that point I concluded that the DCI was of little value at present. So what was going on?
Social media have been the focus of much attention in recent weeks over their unwillingness/tardiness in applying their own rules. Whether it’s Twitter refusing to consider Trump’s aggressive verbal threats against North Korea to be in violation of their harassment policy, or YouTube belatedly removing a video by Logan Paul showing a suicide victim (Matsakis 2018, Meyer 2018), or US and UK government attempts to hold Facebook and Twitter to account over ‘fake news’ (e.g. Hern 2017a, 2017b), there is a growing recognition that not only are ‘we’ the data for these social media behemoths, but that these platforms are optimised for this kind of abuse (Wachter-Boettcher 2017, Bridle 2017).
The US Department of Immigration and Customs Enforcement (ICE) is apparently seeking to employ ‘big data’ methods for automating their assessment of visa applications in pursuit of meeting Trump’s calls for ‘extreme vetting’ (e.g. Joseph 2017, Joseph and Lipp 2017, and see also). A crucial problem with the proposals has been flagged in a letter to the Acting Secretary of Homeland Security by a group of scientists, engineers and others with experience in machine learning, data mining etc.. Specifically, they point to the problem that algorithms developed to detect ‘persons of interest’ could arbitrarily select groups while at the same time appearing to be objective. We’ve already seen this stereotyping and discrimination being embedded in other applications, inadvertently for the most part, and the risk is the same in this case. The reason provided in the letter is simple:
“Inevitably, because these characteristics are difficult (if not impossible) to define and measure, any algorithm will depend on ‘proxies’ that are more easily observed and may bear little or no relationship to the characteristics of interest” (Abelson et al 2017)
The US Department of Immigration and Customs Enforcement (ICE) is apparently seeking to employ ‘big data’ methods for automating their assessment of visa applications in pursuit of meeting Trump’s calls for ‘extreme vetting’ (e.g. 